使用谷歌地球引擎的机器学习和基于现场的水文地质评估,在干旱的绿洲环境中监测荒漠化
Adil Moumane1, Youssef Azougarh2,3, Abdelhaq Ait Enajar4
1Department of Geography, Faculty of Humanities and Social Sciences, Ibn Tofail University, Kenitra, 14000, Morocco. adil.moumane@uit.ac.ma.
Scientific reports
|February 21, 2026
概括
由于气候变化和地下水过度提取,绿洲生态系统的荒漠化正在加速. 这项在摩洛哥的研究显示,土地退化严重,影响水资源和粮食安全.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 绿洲生态系统对于干旱地区至关重要,但易受退化影响.
- 气候压力,土壤脆弱性和地下水过度使用威胁到绿洲的可持续性.
研究的目的:
- 为了评估摩洛哥特纳塔绿洲的荒漠化动态.
- 整合遥感,机器学习,水文地质学和社会经济数据进行全面分析.
主要方法:
- 使用了在谷歌地球引擎中处理的Landsat档案 (1984-2024年).
- 应用了梯度树增强 (GTB) 模型来绘制土地退化地图,达到87.2%的准确性.
- 综合水文地质实地调查和农民采访以获取上下文数据.
主要成果:
- 植被健康的显著下降和荒漠化土地的扩张.
- 地下水位明显下降,水的盐度增加.
- 农民报告说,度压力增加,井成本高,火灾增加.
结论:
- 气候变化和人类过度开发加速了绿洲的荒漠化.
- 迫切需要综合水和土地管理战略,以应对绿洲退化.
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